def compare_variable_attributes(fdatasets, r, name_list, sDir):
vars_df = pd.DataFrame()
for ii in range(len(fdatasets)):
print('\n', fdatasets[ii].split('/')[-1])
deployment = fdatasets[ii].split('/')[-1].split('_')[0].split('deployment')[-1]
deployment = int(deployment)
ds = xr.open_dataset(fdatasets[ii], mask_and_scale=False)
time = ds['time'].values
dr_dp = '-'.join((str(deployment))) #,ds.collection_method, ds.stream
'''
variable list
'''
var_list = cf.notin_list(ds.data_vars.keys(), ['time', '_qc_'])
z_id, z_data, z_unit, z_name, z_fill = cf.add_pressure_to_dictionary_of_sci_vars(ds)
df = pd.DataFrame({'var_id':[z_id], 'units':[z_unit[0]], 'long_name':[z_name[0]], 'fill_values':[z_fill[0]]},index=[dr_dp])
vars_df = vars_df.append(df)
for vname in name_list:
vname_id, vname_unit, vname_name, vname_fv = get_variable_data(ds, var_list, vname)
df = pd.DataFrame({'var_id':[vname_id], 'units':[vname_unit],
'long_name':[vname_name], 'fill_values':[str(vname_fv)]},index=[dr_dp])
vars_df = vars_df.append(df)
vars_df = vars_df.drop_duplicates()
vars_df.to_csv('{}/{}_velocity_variables.csv'.format(sDir, r), index=True)
return vars_df
def plot_velocity_variables(r, fdatasets, num_plots, save_dir):
fig, ax = plt.subplots(nrows=num_plots, ncols=1, sharey=True)
fig.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)
fig_file = 'calculated_currents_plot'
fig0, ax0 = plt.subplots(nrows=num_plots, ncols=1, sharey=True)
fig0.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)
fig0_file = 'uvw_plots'
fig1, ax1 = plt.subplots(nrows=num_plots, ncols=1, sharey=True)
fig1.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)
fig1_file = 'pressure_plots'
fig2, ax2 = plt.subplots(nrows=num_plots, ncols=1, sharey=True)
fig2.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)
fig2_file = 'roll_plots'
fig3, ax3 = plt.subplots(nrows=num_plots, ncols=1, sharey=True)
fig3.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)
fig3_file = 'pitch_plots'
for ii in range(len(fdatasets)):
if num_plots > len(fdatasets):
for jj in range(len(fdatasets), num_plots, 1):
ax[jj].axis('off')
ax0[jj].axis('off')
# ax0[jj].axis('tight')
ax1[jj].axis('off')
ax2[jj].axis('off')
ax3[jj].axis('off')
print('\n', fdatasets[ii].split('/')[-1])
deployment = fdatasets[ii].split('/')[-1].split('_')[0].split(
'deployment')[-1]
deployment = int(deployment)
ds = xr.open_dataset(fdatasets[ii], mask_and_scale=False)
time = ds['time'].values
'''
variable list
'''
var_list = cf.notin_list(ds.data_vars.keys(), ['time', '_qc_'])
z_name, z_data, z_unit, z_name, z_fill = cf.add_pressure_to_dictionary_of_sci_vars(
ds)
z_data, err_count_z = reject_err_data_1_dims(z_data,
z_fill[0],
r,
z_name[0],
n=5)
if 'VELPT' in r:
w_id, w_data, w_unit, w_name, w_fill = get_variable_data(
ds, var_list, 'upward_velocity')
w_data, err_count_w = reject_err_data_1_dims(w_data,
w_fill,
r,
w_id,
n=5)
u_id, u_data, u_unit, u_name, u_fill = get_variable_data(
ds, var_list, 'eastward_velocity')
u_data, err_count_u = reject_err_data_1_dims(u_data,
u_fill,
r,
u_id,
n=5)
v_id, v_data, v_unit, v_name, v_fill = get_variable_data(
ds, var_list, 'northward_velocity')
v_data, err_count_v = reject_err_data_1_dims(v_data,
v_fill,
r,
v_id,
n=5)
roll_id, roll_data, roll_unit, roll_name, roll_fill = get_variable_data(
ds, var_list, 'roll')
roll_data, err_count_roll = reject_err_data_1_dims(roll_data,
roll_fill,
r,
roll_id,
n=5)
pitch_id, pitch_data, pitch_unit, pitch_name, pitch_fill = get_variable_data(
ds, var_list, 'pitch')
pitch_data, err_count_pitch = reject_err_data_1_dims(pitch_data,
pitch_fill,
r,
pitch_id,
n=5)
'''
According to VELPT manufacturer, data are suspect when the instrument is tilted more than 20 degrees
redmine ticket # 12960
'''
tilt_ind = np.logical_or(
abs(pitch_data) > 200,
abs(roll_data) > 200)
percent_good = (
(len(time) - len(time[tilt_ind])) / len(time)) * 100
elif 'VEL3D' in r:
w_id, w_data, w_unit, w_name, w_fill = get_variable_data(
ds, var_list, 'upward_turbulent_velocity')
w_data, err_count_w = reject_err_data_1_dims(w_data,
w_fill,
r,
w_id,
n=5)
u_id, u_data, u_unit, u_name, u_fill = get_variable_data(
ds, var_list, 'eastward_turbulent_velocity')
u_data, err_count_u = reject_err_data_1_dims(u_data,
u_fill,
r,
u_id,
n=5)
v_id, v_data, v_unit, v_name, v_fill = get_variable_data(
ds, var_list, 'northward_turbulent_velocity')
v_data, err_count_v = reject_err_data_1_dims(v_data,
v_fill,
r,
v_id,
n=5)
'''
Plot pressure
'''
ax1[ii].plot(time,
z_data,
'b.',
linestyle='None',
marker='.',
markersize=0.5) #linestyle='--', linewidth=.6
if 'VELPT' in r:
ax1[ii].plot(time[tilt_ind],
z_data[tilt_ind],
'r.',
linestyle='None',
marker='.',
markersize=0.5,
label=str(round(100 - percent_good, 2)) + '%')
prepare_axis(r,
time,
deployment,
ax1[ii],
ii,
len(fdatasets),
z_name[0],
z_unit[0],
err_count=err_count_z)
fig1_file = fig1_file + str(deployment)
if 'VELPT' in r:
'''
plot roll
'''
ax2[ii].plot(time,
roll_data,
'b.',
linestyle='None',
marker='.',
markersize=0.5)
ax2[ii].plot(time[tilt_ind],
roll_data[tilt_ind],
'r.',
linestyle='None',
marker='.',
markersize=0.5,
label=str(round(100 - percent_good, 2)) + '%')
prepare_axis(r,
time,
deployment,
ax2[ii],
ii,
len(fdatasets),
roll_name,
roll_unit,
err_count=err_count_roll)
fig2_file = fig2_file + str(deployment)
'''
plot pitch
'''
ax3[ii].plot(time,
pitch_data,
'b.',
linestyle='None',
marker='.',
markersize=0.5)
ax3[ii].plot(time[tilt_ind],
pitch_data[tilt_ind],
'r.',
linestyle='None',
marker='.',
markersize=0.5,
label=str(round(100 - percent_good, 2)) + '%')
prepare_axis(r,
time,
deployment,
ax3[ii],
ii,
len(fdatasets),
pitch_name,
pitch_unit,
err_count=err_count_pitch)
fig3_file = fig3_file + str(deployment)
'''
1D Quiver plot
'''
ax[ii].quiver(time,
0,
u_data,
v_data,
color='b',
units='y',
scale_units='y',
scale=1,
headlength=1,
headaxislength=1,
width=0.004,
alpha=0.5)
if 'VELPT' in r:
ax[ii].quiver(time[tilt_ind],
0,
u_data[tilt_ind],
v_data[tilt_ind],
color='r',
units='y',
scale_units='y',
scale=1,
headlength=1,
headaxislength=1,
width=0.004,
alpha=0.5,
label=str(round(100 - percent_good, 2)) + '%')
uv_magnitude = np.sqrt(u_data**2 + v_data**2)
uv_maxmag = np.nanmax(uv_magnitude)
ax[ii].set_ylim(-uv_maxmag, uv_maxmag)
prepare_axis(r,
time,
deployment,
ax[ii],
ii,
len(fdatasets),
'Current Velocity',
u_unit,
err_count=None)
fig_file = fig_file + str(deployment)
'''
Plot u and v components
'''
ax0[ii].plot(time,
v_data,
'b.',
linestyle='None',
marker='.',
markersize=0.5,
label='V')
if 'VELPT' in r:
ax0[ii].plot(time[tilt_ind],
v_data[tilt_ind],
'r',
linestyle='None',
marker='.',
markersize=0.5,
label=str(round(100 - percent_good, 2)) + '%')
ax0[ii].plot(time,
u_data,
'g.',
linestyle='None',
marker='.',
markersize=0.5,
label='U')
if 'VELPT' in r:
ax0[ii].plot(time[tilt_ind],
u_data[tilt_ind],
'y',
linestyle='None',
marker='.',
markersize=0.5,
label=str(round(100 - percent_good, 2)) + '%')
ax0[ii].plot(time,
w_data,
'm.',
linestyle='None',
marker='.',
markersize=0.5,
label='W')
if 'VELPT' in r:
ax0[ii].plot(time[tilt_ind],
w_data[tilt_ind],
'c',
linestyle='None',
marker='.',
markersize=0.5,
label=str(round(100 - percent_good, 2)) + '%')
prepare_axis(r,
time,
deployment,
ax0[ii],
ii,
len(fdatasets),
'Velocity Components',
u_unit,
err_count=None)
fig0_file = fig0_file + str(deployment)
save_file = os.path.join(save_dir, fig1_file)
fig1.savefig(str(save_file), dpi=150, bbox_inches='tight')
save_file = os.path.join(save_dir, fig_file)
fig.savefig(str(save_file), dpi=150, bbox_inches='tight')
save_file = os.path.join(save_dir, fig0_file)
fig0.savefig(str(save_file), dpi=150, bbox_inches='tight')
save_file = os.path.join(save_dir, fig2_file)
fig2.savefig(str(save_file), dpi=150, bbox_inches='tight')
save_file = os.path.join(save_dir, fig3_file)
fig3.savefig(str(save_file), dpi=150, bbox_inches='tight')
plt.close('all')
def plot_velocity_variables(r, fdatasets, num_plots, save_dir):
fig0_0x, ax0_0x = pyplot.subplots(nrows=num_plots, ncols=1, sharey=True)
fig0_0x.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)
fig0_0x_file = 'v_plots_xsection'
fig0_0p, ax0_0p = pyplot.subplots(nrows=num_plots, ncols=1, sharey=True)
fig0_0p.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)
fig0_0p_file = 'v_plots_profile'
fig0_1x, ax0_1x = pyplot.subplots(nrows=num_plots, ncols=1, sharey=True)
fig0_1x.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)
fig0_1x_file = 'u_plots_xsection'
fig0_1p, ax0_1p = pyplot.subplots(nrows=num_plots, ncols=1, sharey=True)
fig0_1p.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)
fig0_1p_file = 'u_plots_profile'
fig0_2x, ax0_2x = pyplot.subplots(nrows=num_plots, ncols=1, sharey=True)
fig0_2x.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)
fig0_2x_file = 'w_plots_xsection'
fig0_2p, ax0_2p = pyplot.subplots(nrows=num_plots, ncols=1, sharey=True)
fig0_2p.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)
fig0_2p_file = 'w_plots_profile'
# fig1, ax1 = pyplot.subplots(nrows=num_plots, ncols=1, sharey=True)
# fig1.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)
# fig1_file = 'Calculated_current_plots'
fig2x, ax2x = pyplot.subplots(nrows=num_plots, ncols=1, sharey=True)
fig2x.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)
fig2x_file = 'roll_plots_xsection'
fig2p, ax2p = pyplot.subplots(nrows=num_plots, ncols=1, sharey=True)
fig2p.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)
fig2p_file = 'roll_plots_profile'
fig3x, ax3x = pyplot.subplots(nrows=num_plots, ncols=1, sharey=True)
fig3x.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)
fig3x_file = 'pitch_plots_xsection'
fig3p, ax3p = pyplot.subplots(nrows=num_plots, ncols=1, sharey=True)
fig3p.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)
fig3p_file = 'pitch_plots_profile'
fig4x, ax4x = pyplot.subplots(nrows=num_plots, ncols=1, sharey=True)
fig4x.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)
fig4x_file = 'calculated_current_magnitude_plots_xsection'
fig4p, ax4p = pyplot.subplots(nrows=num_plots, ncols=1, sharey=True)
fig4p.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)
fig4p_file = 'calculated_current_magnitude_plots_profile'
if num_plots > len(fdatasets):
for jj in range(len(fdatasets), num_plots, 1):
ax0_0x[jj].axis('off')
ax0_1x[jj].axis('off')
ax0_2x[jj].axis('off')
ax0_0p[jj].axis('off')
ax0_1p[jj].axis('off')
ax0_2p[jj].axis('off')
# ax1[jj].axis('off')
ax2p[jj].axis('off')
ax2x[jj].axis('off')
ax3x[jj].axis('off')
ax4x[jj].axis('off')
ax3p[jj].axis('off')
ax4p[jj].axis('off')
if len(fdatasets) == 1:
ax0_0x = [ax0_0x]
ax0_1x = [ax0_1x]
ax0_2x = [ax0_2x]
ax0_0p = [ax0_0p]
ax0_1p = [ax0_1p]
ax0_2p = [ax0_2p]
# ax1 = [ax1]
ax2p = [ax2p]
ax2x = [ax2x]
ax3x = [ax3x]
ax4x = [ax4x]
ax3p = [ax3p]
ax4p = [ax4p]
for ii in range(len(fdatasets)):
print('\n', fdatasets[ii].split('/')[-1])
deployment = fdatasets[ii].split('/')[-1].split('_')[0].split('deployment')[-1]
deployment = int(deployment)
ds = xr.open_dataset(fdatasets[ii], mask_and_scale=False)
time = ds['time'].values
collection_method = ds.collection_method
'''
variable list
'''
var_list = cf.notin_list(ds.data_vars.keys(), ['time', '_qc_'])
z_data, z_unit, z_name, z_fill = cf.add_pressure_to_dictionary_of_sci_vars(ds)
z_data, err_count_z = reject_err_data_1_dims(z_data, z_fill[0], r, z_name[0], n=5)
w_id, w_data, w_unit, w_name, w_fill = get_variable_data(ds, var_list, 'upward_velocity')
w_data, err_count_w = reject_err_data_1_dims(w_data, w_fill, r, w_id, n=5)
u_id, u_data, u_unit, u_name, u_fill = get_variable_data(ds, var_list, 'eastward_velocity')
u_data, err_count_u = reject_err_data_1_dims(u_data, u_fill, r, u_id, n=5)
v_id, v_data, v_unit, v_name, v_fill = get_variable_data(ds, var_list, 'northward_velocity')
v_data, err_count_v = reject_err_data_1_dims(v_data, v_fill, r, v_id, n=5)
roll_id, roll_data, roll_unit, roll_name, roll_fill = get_variable_data(ds, var_list, 'roll')
roll_data, err_count_roll = reject_err_data_1_dims(roll_data, roll_fill, r, roll_id, n=5)
pitch_id, pitch_data, pitch_unit, pitch_name, pitch_fill = get_variable_data(ds, var_list, 'pitch')
pitch_data, err_count_pitch = reject_err_data_1_dims(pitch_data, pitch_fill, r, pitch_id, n=5)
'''
2D Quiver plot
'''
# ax1[ii].quiver(time, z_data, u_data, v_data, color='b', units='y', scale_units='y', scale=1, headlength=1,
# headaxislength=1, width=0.004, alpha=0.5)
# M = np.sqrt(u_data ** 2 + v_data ** 2)
# Q = ax1[ii].quiver(time[::100], z_data[::100], u_data[::100], v_data[::100], M[::100],
# units='y', pivot='tip', width=0.022, scale=1 / 0.15)
# css = ax1[ii].quiverkey(Q, 0.9, 0.9, 1, r'$1 \frac{m}{s}$', labelpos='E', coordinates='figure')
# #
# prepare_axis(css, fig1, ax1[ii],time, [z_name[0], z_unit[0]], ['Current Velocity', u_unit], r, deployment, ii, len(fdatasets),
# err_count=None)
#
# fig1_file = fig1_file + str(deployment)
#
'''
plot roll
'''
# css = ax2p[ii].scatter(roll_data, z_data, c=time, s=2, edgecolor='None', cmap='rainbow')
# prepare_axis_profile(css, fig2p, ax2p[ii], time, [z_name[0], z_unit[0]], [roll_name, roll_unit], r, deployment, ii,
# len(fdatasets), err_count=err_count_roll)
# fig2p_file = fig2p_file + '_deployment' + str(deployment)+ '_' + \
# fdatasets[ii].split('/')[-1].split('_')[-1].split('.')[0]
#
#
# css = ax2x[ii].scatter(time, z_data, c=roll_data, cmap='RdGy', s=2, edgecolor='None')
# prepare_axis_xsection(css, fig2x, ax2x[ii], time, [z_name[0], z_unit[0]], [roll_name, roll_unit], r, deployment, ii,
# len(fdatasets), err_count=err_count_roll)
# fig2x_file = fig2x_file + '_deployment' + str(deployment) + '_' + \
# fdatasets[ii].split('/')[-1].split('_')[-1].split('.')[0]
# #
'''
plot pitch
'''
# css = ax3p[ii].scatter(pitch_data, z_data, c=time, s=2, edgecolor='None', cmap='rainbow')
# prepare_axis_profile(css, fig3p, ax3p[ii], time, [z_name[0], z_unit[0]], [pitch_name, pitch_unit], r, deployment, ii,
# len(fdatasets), err_count=err_count_pitch)
# fig3p_file = fig3p_file + '_deployment' + str(deployment) + '_' + fdatasets[ii].split('/')[-1].split('_')[-1].split('.')[0]
#
# css = ax3x[ii].scatter(time, z_data, c=pitch_data, cmap='RdGy', s=2, edgecolor='None')
# prepare_axis_xsection(css, fig3x, ax3x[ii], time, [z_name[0], z_unit[0]], [pitch_name, pitch_unit], r, deployment, ii,
# len(fdatasets), err_count=err_count_pitch)
# fig3x_file = fig3x_file + '_deployment' + str(deployment) + '_' + fdatasets[ii].split('/')[-1].split('_')[-1].split('.')[0]
#
# '''
# plot current magnitude
# '''
# uv_magnitude = np.sqrt(u_data ** 2 + v_data ** 2)
#
# css = ax4p[ii].scatter(uv_magnitude, z_data, c=time, s=2, edgecolor='None', cmap='rainbow')
# prepare_axis_profile(css, fig4p, ax4p[ii], time, [z_name[0], z_unit[0]], ['[U,V] Current Velocity', u_unit],
# r, deployment, ii, len(fdatasets), err_count=err_count_pitch)
#
# fig4p_file = fig4p_file + '_deployment' + str(deployment) + '_' + \
# fdatasets[ii].split('/')[-1].split('_')[-1].split('.')[0]
#
# css = ax4x[ii].scatter(time, z_data, c=uv_magnitude, cmap='PuBu', s=2, edgecolor='None')
# prepare_axis_xsection(css, fig4x, ax4x[ii], time, [z_name[0], z_unit[0]], ['[U,V] Current Velocity', u_unit],
# r, deployment, ii, len(fdatasets), err_count=None)
# fig4x_file = fig4x_file + '_deployment' + str(deployment) + '_' + \
# fdatasets[ii].split('/')[-1].split('_')[-1].split('.')[0]
#
# '''
# Plot v component
# '''
# css = ax0_0p[ii].scatter(v_data, z_data, c=time, s=2, edgecolor='None', cmap='rainbow')
# prepare_axis_profile(css, fig0_0p, ax0_0p[ii], time, [z_name[0], z_unit[0]], ['[V] Velocity Components', u_unit],
# r, deployment, ii, len(fdatasets), err_count=err_count_pitch)
#
# fig0_0p_file = fig0_0p_file + '_deployment' + str(deployment) + '_' + \
# fdatasets[ii].split('/')[-1].split('_')[-1].split('.')[0]
#
# css = ax0_0x[ii].scatter(time, z_data, c=v_data, cmap='RdBu', s=2, edgecolor='None')
# prepare_axis_xsection(css, fig0_0x, ax0_0x[ii], time, [z_name[0], z_unit[0]], ['[V] Velocity Components', v_unit],
# r, deployment, ii, len(fdatasets), err_count=err_count_v)
# fig0_0x_file = fig0_0x_file + '_deployment' + str(deployment) + '_' + \
# fdatasets[ii].split('/')[-1].split('_')[-1].split('.')[0]
# #
# '''
# Plot u component
# '''
# css = ax0_1p[ii].scatter(u_data, z_data, c=time, s=2, edgecolor='None', cmap='rainbow')
# prepare_axis_profile(css, fig0_1p, ax0_1p[ii], time, [z_name[0], z_unit[0]],
# ['[U] Velocity Components', u_unit],
# r, deployment, ii, len(fdatasets), err_count=err_count_pitch)
#
# fig0_1p_file = fig0_1p_file + '_deployment' + str(deployment) + '_' + \
# fdatasets[ii].split('/')[-1].split('_')[-1].split('.')[0]
#
# css = ax0_1x[ii].scatter(time, z_data, c=u_data, cmap='RdBu', s=2, edgecolor='None')
# prepare_axis_xsection(css, fig0_1x, ax0_1x[ii], time, [z_name[0], z_unit[0]], ['[U] Velocity Components', u_unit],
# r, deployment, ii, len(fdatasets), err_count=err_count_u)
#
# fig0_1x_file = fig0_1x_file + '_deployment' + str(deployment) + '_' + \
# fdatasets[ii].split('/')[-1].split('_')[-1].split('.')[0]
#
# '''
# Plot w component
# '''
css = ax0_2p[ii].scatter(w_data, z_data, c=time, s=2, edgecolor='None', cmap='rainbow')
prepare_axis_profile(css, fig0_2p, ax0_2p[ii], time, [z_name[0], z_unit[0]],
['[W] Velocity Components', u_unit],
r, deployment, ii, len(fdatasets), err_count=err_count_pitch)
fig0_2p_file = fig0_2p_file + '_deployment' + str(deployment) + '_' + \
fdatasets[ii].split('/')[-1].split('_')[-1].split('.')[0]
css = ax0_2x[ii].scatter(time, z_data, c=w_data, cmap='RdBu', s=2, edgecolor='None')
prepare_axis_xsection(css, fig0_2x, ax0_2x[ii], time, [z_name[0], z_unit[0]], ['[W] Velocity Components', w_unit],
r, deployment, ii, len(fdatasets), err_count=err_count_w)
fig0_2x_file = fig0_2x_file + '_deployment' + str(deployment) + '_' + \
fdatasets[ii].split('/')[-1].split('_')[-1].split('.')[0]
#
#
# save_file = os.path.join(save_dir, fig1_file)
# fig1.savefig(str(save_file), dpi=150, bbox_inches='tight')
# save_file = os.path.join(save_dir, fig2p_file)
# fig2p.savefig(str(save_file), dpi=150, bbox_inches='tight')
#
# save_file = os.path.join(save_dir, fig2x_file)
# fig2x.savefig(str(save_file), dpi=150, bbox_inches='tight')
# save_file = os.path.join(save_dir, fig3p_file)
# fig3p.savefig(str(save_file), dpi=150, bbox_inches='tight')
#
# save_file = os.path.join(save_dir, fig3x_file)
# fig3x.savefig(str(save_file), dpi=150, bbox_inches='tight')
# #
# save_file = os.path.join(save_dir, fig4p_file)
# fig4p.savefig(str(save_file), dpi=150, bbox_inches='tight')
#
# save_file = os.path.join(save_dir, fig4x_file)
# fig4x.savefig(str(save_file), dpi=150, bbox_inches='tight')
#
# save_file = os.path.join(save_dir, fig0_0p_file)
# fig0_0p.savefig(str(save_file), dpi=150, bbox_inches='tight')
#
# save_file = os.path.join(save_dir, fig0_0x_file)
# fig0_0x.savefig(str(save_file), dpi=150, bbox_inches='tight')
# #
# save_file = os.path.join(save_dir, fig0_1p_file)
# fig0_1p.savefig(str(save_file), dpi=150, bbox_inches='tight')
#
# save_file = os.path.join(save_dir, fig0_1x_file)
# fig0_1x.savefig(str(save_file), dpi=150, bbox_inches='tight')
# #
save_file = os.path.join(save_dir, fig0_2p_file)
fig0_2p.savefig(str(save_file), dpi=150, bbox_inches='tight')
save_file = os.path.join(save_dir, fig0_2x_file)
fig0_2x.savefig(str(save_file), dpi=150, bbox_inches='tight')
pyplot.close(fig0_0p)
pyplot.close(fig0_0x)
pyplot.close(fig0_1p)
pyplot.close(fig0_1x)
pyplot.close(fig0_2p)
pyplot.close(fig0_2x)
pyplot.close(fig2p)
pyplot.close(fig2x)
pyplot.close(fig3p)
pyplot.close(fig3x)
pyplot.close(fig4p)
pyplot.close(fig4x)